BNI Sekuritas is building its AI capability to drive smarter, more efficient, and scalable solutions across the firm. We're looking for an AI Engineer (third party contract) to design, build, and deploy production-grade AI and Generative AI solutions, from RAG pipelines and AI agents to cloud-based and on-premise LLM deployments. This is a hands-on engineering role. You'll work closely with technology and business teams to turn AI use cases into reliable, secure, and scalable solutions. You'll be involved across the full AI engineering lifecycle from model and architecture selection, experimentation, and development to deployment, monitoring, and continuous improvement. What you'll do - Design, build, and deploy Generative AI and agentic AI solutions on enterprise cloud AI platforms such as Google Vertex AI, Azure AI Foundry, Alibaba Model Studio, or AWS Bedrock - Develop production-ready RAG pipelines, including embedding, chunking, retrieval, reranking, and vector database implementation - Build and orchestrate AI agents and multi-agent workflows using LangChain/LangGraph, LlamaIndex, Semantic Kernel, CrewAI, or AutoGen - Develop AI services and integrations using Python, APIs, microservices, Docker, and Kubernetes - Work with Llama, Qwen, Mistral, GPT, Gemini, or equivalent models to develop and optimize AI applications - Apply prompt engineering and lightweight fine-tuning techniques, including LoRA/QLoRA - Design model evaluation, monitoring, and performance-improvement processes - Apply MLOps practices including model/version management, experiment tracking, and CI/CD - Collaborate with IT, data, and business stakeholders - Ensure compliance with IAM, data governance, security, and cost-management requirements - Evaluate AI architecture, models, frameworks, and cloud services - Contribute to internal AI engineering standards and reusable components Nice to have: experience with on-premise LLM inference engines (vLLM, TGI, Triton, Ollama, llama.cpp), GPU infrastructure, financial services/banking/capital markets, sensitive or regulated data, AI governance, responsible AI, model risk management, and security.